How to Use 100+ AI Models with a Single API (OpenRouter, AIMLAPI & More)

Once you’ve discovered tools and checked benchmarks, the next step is to actually use and compare multiple models in your stack. Doing this directly with each provider quickly becomes painful: separate keys, auth, SDKs, rate limits, and invoices.

Unified AI APIs solve this by exposing dozens or hundreds of models behind a single endpoint.

This guide covers the most useful unified access platforms today, and shows how to use them to test, route, and swap models without rewriting your app.

What Is a Unified AI API?

A unified AI API (or AI gateway) is a service that:

• Connects to many providers (OpenAI, Anthropic, Google, Meta, Mistral, etc.).
• Exposes a single, mostly standard API (often OpenAI-compatible).
• Lets you choose models by a simple model string.
• Adds features like routing, fallbacks, logging, and cost controls.

Instead of integrating 10 vendors, you integrate one gateway, then change model IDs.

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Top Unified Access Platforms (2026)

1. OpenRouter

• What it is: Unified API gateway giving access to hundreds of models from dozens of providers through a single endpoint, with OpenAI-compatible syntax.
• Best for: Teams that want to iterate quickly across many models and providers with minimal code changes.
• Key strengths:
  • OpenAI-compatible API (“just change the base URL” in many cases)
  • Centralized billing for multiple providers
  • Automatic fallbacks and routing options (optimize for speed, cost, or quality)
  • Support for chat, completions, images, and other modalities where models allow
• Link: OpenRouter

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2. AIMLAPI.com

• What it is: Unified AI API that exposes 400+ models with a focus on low-cost experimentation and easy migration away from single-vendor setups.
• Best for: Cost-sensitive teams that want to try many models cheaply and support multi-modal (text, image, embeddings, TTS) workflows.
• Key strengths:
  • Large model catalog (text, image, embeddings, audio)
  • Competitive pricing; often positioned as cheaper than baseline OpenAI
  • Playground for quick testing without writing code
  • Simple API with model routing options
• Link: AIMLAPI

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3. Vercel AI SDK (and Vercel v0)

• What it is: An open-source SDK and React/Next.js primitives (useChat, useCompletion) that make it easy to call multiple AI providers (OpenAI, Anthropic, OpenRouter, custom) from one unified interface. Often used together with Vercel’s v0 and AI templates for rapid prototyping.
• Best for: Frontend-heavy teams building Next.js or Vercel-hosted apps who want a clean developer experience for chatting with different models without wiring raw HTTP calls.
• Key strengths:
  • Unified API on the client and server side; plug in different providers via config
  • Built-in streaming support and UI components for chat/completions
  • Plays nicely with gateways like OpenRouter/AIMLAPI as backends
• Link: Vercel AI SDK

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4. PremAI (Unified LLM API)

• What it is: Unified LLM API and gateway focused on enterprise use, connecting to multiple providers with governance, observability, and routing controls.
• Best for: Teams that need more enterprise-style controls (access policies, monitoring, governance) on top of multi-model access.
• Key strengths:
  • Multi-provider LLM gateway with routing
  • Governance features (access control, audit trails)
  • Monitoring and analytics for requests and costs
• Link: PremAI

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5. LiteLLM (Open-Source Gateway)

• What it is: Open-source library and proxy that gives you a unified, OpenAI-like API over many providers (OpenAI, Anthropic, Azure, etc.). You can self-host it.
• Best for: Teams that want gateway-style behavior but prefer running it themselves for privacy, cost, or customization reasons.
• Key strengths:
  • Open-source and self-hostable
  • OpenAI-compatible API surface
  • Flexible configuration for routing to multiple backends
• Link: LiteLLM

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6. Portkey

• What it is: AI gateway that provides unified APIs across multiple providers, with features like semantic routing, caching, observability, and governance.
• Best for: Production teams that want advanced routing and traffic management (for example, routing based on intent or cost) without building their own gateway.
• Key strengths:
  • Dynamic routing and percentage-based traffic splits
  • Semantic caching for cost savings
  • Observability (metrics, traces, logs)
  • Policy and governance controls
• Link: Portkey

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7. Bifrost / Other AI Gateways

• What it is: A new class of AI gateways (for example, Bifrost) focused on multi-model routing and cost/latency optimization across providers.
• Best for: Teams that want gateway-first architecture, with routing logic, budget limits, and observability baked in.
• Key strengths:
  • Model routing by intent, cost, or performance
  • Semantic caching
  • Strong integration with monitoring/observability stacks
• Link: Bifrost

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8. MindStudio / Similar Routing Tools

• What it is: Platforms like MindStudio that include model routers and orchestration layers for multi-provider LLM setups.
• Best for: Teams that want visual or higher-level orchestration (graphs, flows) rather than hand-written routing code.
• Key strengths:
  • Graph-based routing logic (intent classification → model selection)
  • Connectors for many providers
  • Controls for sensitive-data routing and policies
• Link: MindStudio

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9. Code-Level Abstractions (Your Own Adapter Layer)

• What it is: A small internal library in your codebase that standardizes how you call any model, regardless of vendor.
• Best for: Any team serious about model flexibility; this is more pattern than product.
• Key strengths:
  • Works with or without an external gateway
  • Lets you swap models by changing configuration, not business logic
  • Forms the foundation for later routing and A/B testing